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Impact of COVID-19 on epidemic trend of hepatitis C in Henan Province assessed by interrupted time series analysis
OBJECTIVE: Hepatitis C presents a profound global health challenge. The impact of COVID-19 on hepatitis C, however, remain uncertain. This study aimed to ascertain the influence of COVID-19 on the hepatitis C epidemic trend in Henan Province. METHODS: We collated the number of monthly diagnosed case...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10580576/ https://www.ncbi.nlm.nih.gov/pubmed/37848842 http://dx.doi.org/10.1186/s12879-023-08635-9 |
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author | Li, Yanyan Li, Xinxiao Lan, Xianxiang Xue, Chenlu Zhang, Bingjie Wang, YongBin |
author_facet | Li, Yanyan Li, Xinxiao Lan, Xianxiang Xue, Chenlu Zhang, Bingjie Wang, YongBin |
author_sort | Li, Yanyan |
collection | PubMed |
description | OBJECTIVE: Hepatitis C presents a profound global health challenge. The impact of COVID-19 on hepatitis C, however, remain uncertain. This study aimed to ascertain the influence of COVID-19 on the hepatitis C epidemic trend in Henan Province. METHODS: We collated the number of monthly diagnosed cases in Henan Province from January 2013 to September 2022. Upon detailing the overarching epidemiological characteristics, the interrupted time series (ITS) analysis using autoregressive integrated moving average (ARIMA) models was employed to estimate the hepatitis C diagnosis rate pre and post the COVID-19 emergence. In addition, we also discussed the model selection process, test model fitting, and result interpretation. RESULTS: Between January 2013 and September 2022, a total of 267,968 hepatitis C cases were diagnosed. The yearly average diagnosis rate stood at 2.42/100,000 persons. While 2013 witnessed the peak diagnosis rate at 2.97/100,000 persons, 2020 reported the least at 1.7/100,000 persons. The monthly mean hepatitis C diagnosed numbers culminated in 2291 cases. The optimal ARIMA model chosen was ARIMA (0,1,1) (0,1,1)(12) with AIC = 1459.58, AICc = 1460.19, and BIC = 1472.8; having coefficients MA1=-0.62 (t=-8.06, P < 0.001) and SMA1=-0.79 (t=-6.76, P < 0.001). The final model’s projected step change was − 800.0 (95% confidence interval [CI] -1179.9 ~ -420.1, P < 0.05) and pulse change was 463.40 (95% CI 191.7 ~ 735.1, P < 0.05) per month. CONCLUSION: The measures undertaken to curtail COVID-19 led to a diminishing trend in the diagnosis rate of hepatitis C. The ARIMA model is a useful tool for evaluating the impact of large-scale interventions, because it can explain potential trends, autocorrelation, and seasonality, and allow for flexible modeling of different types of impacts. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-023-08635-9. |
format | Online Article Text |
id | pubmed-10580576 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105805762023-10-18 Impact of COVID-19 on epidemic trend of hepatitis C in Henan Province assessed by interrupted time series analysis Li, Yanyan Li, Xinxiao Lan, Xianxiang Xue, Chenlu Zhang, Bingjie Wang, YongBin BMC Infect Dis Research OBJECTIVE: Hepatitis C presents a profound global health challenge. The impact of COVID-19 on hepatitis C, however, remain uncertain. This study aimed to ascertain the influence of COVID-19 on the hepatitis C epidemic trend in Henan Province. METHODS: We collated the number of monthly diagnosed cases in Henan Province from January 2013 to September 2022. Upon detailing the overarching epidemiological characteristics, the interrupted time series (ITS) analysis using autoregressive integrated moving average (ARIMA) models was employed to estimate the hepatitis C diagnosis rate pre and post the COVID-19 emergence. In addition, we also discussed the model selection process, test model fitting, and result interpretation. RESULTS: Between January 2013 and September 2022, a total of 267,968 hepatitis C cases were diagnosed. The yearly average diagnosis rate stood at 2.42/100,000 persons. While 2013 witnessed the peak diagnosis rate at 2.97/100,000 persons, 2020 reported the least at 1.7/100,000 persons. The monthly mean hepatitis C diagnosed numbers culminated in 2291 cases. The optimal ARIMA model chosen was ARIMA (0,1,1) (0,1,1)(12) with AIC = 1459.58, AICc = 1460.19, and BIC = 1472.8; having coefficients MA1=-0.62 (t=-8.06, P < 0.001) and SMA1=-0.79 (t=-6.76, P < 0.001). The final model’s projected step change was − 800.0 (95% confidence interval [CI] -1179.9 ~ -420.1, P < 0.05) and pulse change was 463.40 (95% CI 191.7 ~ 735.1, P < 0.05) per month. CONCLUSION: The measures undertaken to curtail COVID-19 led to a diminishing trend in the diagnosis rate of hepatitis C. The ARIMA model is a useful tool for evaluating the impact of large-scale interventions, because it can explain potential trends, autocorrelation, and seasonality, and allow for flexible modeling of different types of impacts. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12879-023-08635-9. BioMed Central 2023-10-17 /pmc/articles/PMC10580576/ /pubmed/37848842 http://dx.doi.org/10.1186/s12879-023-08635-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Li, Yanyan Li, Xinxiao Lan, Xianxiang Xue, Chenlu Zhang, Bingjie Wang, YongBin Impact of COVID-19 on epidemic trend of hepatitis C in Henan Province assessed by interrupted time series analysis |
title | Impact of COVID-19 on epidemic trend of hepatitis C in Henan Province assessed by interrupted time series analysis |
title_full | Impact of COVID-19 on epidemic trend of hepatitis C in Henan Province assessed by interrupted time series analysis |
title_fullStr | Impact of COVID-19 on epidemic trend of hepatitis C in Henan Province assessed by interrupted time series analysis |
title_full_unstemmed | Impact of COVID-19 on epidemic trend of hepatitis C in Henan Province assessed by interrupted time series analysis |
title_short | Impact of COVID-19 on epidemic trend of hepatitis C in Henan Province assessed by interrupted time series analysis |
title_sort | impact of covid-19 on epidemic trend of hepatitis c in henan province assessed by interrupted time series analysis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10580576/ https://www.ncbi.nlm.nih.gov/pubmed/37848842 http://dx.doi.org/10.1186/s12879-023-08635-9 |
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